Building Types

This example demonstrates how to map dwelling units by primary rensidence status and structural type for primary residences. ## Data First we grab the building data via cancensus, making use of the CensusMapper API tool to select the regions and variables we need.

#devtools::install_github("mountainmath/cancensus")
library(cancensus)
library(dotdensity)
library(cancensusHelpers)
# options(cancensus.api_key='your_api_key')
dataset='CA16'
regions=list(CSD="3520005")
vectors <- search_census_vectors("structural type of dwelling",dataset) %>%
  child_census_vectors(TRUE) %>%  
  pull(vector)

We choose the categories and colours we want to map and define a convenience function to rename the variables and compute the qantities for the other asian countries that we don’t break out.

labels=list(v_CA16_417="Movable Dwelling",                                  
            v_CA16_409="Single Detached",
            v_CA16_416="Other Single Attached",
            v_CA16_414="Duplex",
            v_CA16_412="Semi Detached",
            v_CA16_413="Row House",
            v_CA16_415="Apartment, < 5 storeys",
            v_CA16_410="Apartment, >= 5 storeys")
categories=c("Non-primary Residence",as.character(labels))
colors=setNames(
  c("#cccccc","#7a0177", "#ffff00", "#ffcc00", "#ff6600", "#ff0066", "#ff00ff", "#4dff4a", "#00ffff"),
  categories)
prep_data <- function(geo){
  data <- geo %>% replace(is.na(.), 0)
  names(data)[names(data) %in% names(labels)] <- as.character(labels[names(data)[names(data) %in% names(labels)]])
  data %>% mutate(`Non-primary Residence`=Dwellings - Households)
}

Next we grab the data via cancensus,

data_csd=get_census(dataset = dataset, regions=regions,vectors=vectors,geo_format='sf',labels='short',level='CSD') %>% prep_data
data_ct=get_census(dataset = dataset, regions=regions,vectors=vectors,geo_format='sf',labels='short',level='CT') %>% prep_data
data_da=get_census(dataset = dataset, regions=regions,vectors=vectors,geo_format='sf',labels='short',level='DA') %>% prep_data
data_db=get_census(dataset = dataset, regions=regions,geo_format='sf',labels='short',level='DB')

which we then re-aggregate to make sure we don’t miss overall counts due to privacy cutoffs distribute them proportionally among the population.

data_ct <- dot_density.proportional_re_aggregate(data=data_ct,
                                                      parent_data=data_csd,
                                                      geo_match=setNames("GeoUID","CSD_UID"),
                                                      categories=categories,base="Dwellings")
data_da <- dot_density.proportional_re_aggregate(data=data_da,
                                                      parent_data=data_ct,
                                                      geo_match=setNames("GeoUID","CT_UID"),
                                                      categories=categories,base="Dwellings")
data_db <- dot_density.proportional_re_aggregate(data=data_db,
                                                      parent_data=data_da,
                                                      geo_match=setNames("GeoUID","DA_UID"),
                                                      categories=categories,base="Dwellings")

Map

All that’s left to do is to covert our re-aggregated block-level data to dots, using the dot_density.compute_dots function from the dotdensity package and feed it into the dot_density.dots_map function to add them to our basemap.